Detecting k-Balanced Trusted Cliques in Signed Social Networks

Fei Hao, Sik-Sang Yau, Geyong Min, Laurence T. Yang

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


k-Clique detection enables computer scientists and sociologists to analyze social networks' latent structure and thus understand their structural and functional properties. However, the existing k-clique-detection approaches are not applicable to signed social networks directly because of positive and negative links. The authors' approach to detecting k-balanced trusted cliques in such networks bases the detection algorithm on formal context analysis. It constructs formal contexts using the modified adjacency matrix after converting a signed social network into an unweighted one. Experimental results demonstrate that their algorithm can efficiently identify the trusted cliques.

Original languageEnglish (US)
Article number6777472
Pages (from-to)24-31
Number of pages8
JournalIEEE Internet Computing
Issue number2
StatePublished - Mar 1 2014


  • FCA
  • equiconcept
  • signed social networks
  • trusted cliques

ASJC Scopus subject areas

  • Computer Networks and Communications


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